Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation

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• The canonicalization approach maintains performance advantage in few-step generation scenarios.result 85%
• Canonical diffusion models achieve superior expressivity over invariant models for symmetry-invariant generative tasks.technical 85%
• Canonicalization accelerates training of diffusion models by reducing the conditional variance in flow matching.method 90%
• Canonical diffusion significantly outperforms equivariant baselines in 3D molecule generation tasks.result 90%
• CanonFlow achieves state-of-the-art performance on the GEOM-DRUG dataset.result 90%
• The canonicalization framework can be instantiated for molecular graph generation under permutation and rotation symmetries.method 95%
• Aligned priors and optimal transport complement canonicalization to further improve training efficiency.technical 85%
• The canonicalization approach assumes a mathematical background that may not hold in all cases and could introduce biases if improperly handled.limitation 80%

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